A new approach for oil tank detection using deep learning features with control false alarm rate in high-resolution satellite imagery

Zalpour, Moein; Akbarizadeh, Gholamreza; Alaei-Sheini, Navid

doi:10.1080/01431161.2019.1685720

Cited by 60 publications

(31 citation statements)

References 17 publications

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“…N OWADAYS, deep convolutional neural networks have gained much attention in the application of synthetic aperture radar (SAR) field, such as automatic target recognition [1], urban interpretation [2], marine surveillance [3] and so on [4]. Among them, ship detection in SAR images has been widely studied due to its indispensable role in military and civil fields.…”

Section: Introductionmentioning

confidence: 99%

Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation

Chen

Zhan

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation

Chen

Zhan

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Deep learning based methods [22]- [24] extract the features from the images in a joint spatial-spectral manner. It has turned out to be good at modeling the intricate structures hidden in high-resolution images for segmentation and classification tasks [25]. In [15], a convolutional-wavelet neural network is proposed to compute structure features that account for the neighborhood of an individual pixel.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

Zhang

Liu

Jiao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Patch-level labels are easy to achieve, and they require less expertise and lower resource consumption than pixellevel ones. Each patch has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and selection of discriminative features in PTM have a large impact on the classification results. Most of the existing feature learning methods do not make full use of high-level structure feature and the feature correlation within similar images to mine discriminative features. Therefore, this paper proposes a discriminative sketch topic model with structural constraint (C-SSTM) for SAR image classification. In the proposed model, each image patch is characterized by structural and texture features. In particular, the sketch structural feature is based on the sketch map to represent the image local structure pattern. Then the local image manifold information is preserved in terms of structure and texture. In the structural constraint, the texture and structure of each image patch are combined to learn discriminative latent semantic topics between image patches. The structural constraint enforces that the semantically similar features usually co-occur in similar images with a high probability. Finally, each image patch is quantified by discriminative latent semantic topics instead of lowlevel representation. The experimental results tested on synthetic and real SAR images demonstrate that the proposed C-SSTM is able to learn effective structural feature representation from SAR images. Compared with other related approaches, C-SSTM produces competitive classification accuracies with high time efficiency.

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“…Detecting oil depots in satellite images allows civilian and military actors to assess a region's economic asset. Several papers have proposed methods to tackle this problem such as the one described in (Han, Xu, 2012, Cai et al, 2014, Ok, Baseski, 2015, Zhang et al, 2015, Soundrapandiyan, 2017 and more recently in (Zalpour et al, 2019) and (Tadros et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Several machine-learning based oil tank detectors have also been developed in recent years. The authors of (Zhang et al, 2015), (Soundrapandiyan, 2017) and (Zalpour et al, 2019) use feature extractors (SURF, HOG or pretrained neural networks) on top of which a classifier is trained on an annotated dataset. On the other hand, (Tadros et al, 2020) proposes a calibrationfree clustering method controlling the number of false detections.…”

Section: Introductionmentioning

confidence: 99%

Circular-Shaped Object Detection in Low Resolution Satellite Images

Tadros¹,

Drouyer²,

Gioi³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Circle and ellipse detection have been a widely discussed topic among the computer vision community. Applying circle detection methods to satellite images can bring valuable information on urban or industrial areas. A limitation of such methods lies in the resolution of the object to detect. The smaller they are in the image, the harder it becomes to detect them accurately. In this paper, we explore several circle detection methods adapted to low resolution satellite images. An algorithm based on level-lines detection and classification will be presented in this paper along with other well known algorithms. The methods are evaluated in the context of oil tank detection in Sentinel-2 images where those circular objects can have an observed diameter as small as 2 pixels.

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A new approach for oil tank detection using deep learning features with control false alarm rate in high-resolution satellite imagery

Cited by 60 publications

References 17 publications

Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation

Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation

Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

Circular-Shaped Object Detection in Low Resolution Satellite Images

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